The main result of this effectiveness study is that a reading program with a focus on students’ poetry reading processes, based on observational learning via eye movement modeling examples, can improve students’ reading comprehension for different text types. In a pretest-posttest design with an experimental group (ten classes) and a control group (five classes), students’ self-efficacy regarding their own reading process and their reading comprehension were measured. Over a six-week period, teachers of Dutch and their students worked with the six experimental lessons, instead of the regular reading program: students observed and evaluated contrasting peer reading processes, reflected on differences with their own reading process, and then they practiced aspects of a deep reading process. The program resulted in significant progress in the reading comprehension of “expository texts” (ES = .66), “short stories” (ES = .66), and especially “poetry” (ES = .81). Furthermore, the self-efficacy test results show that students in the experimental condition experienced significantly more learning effect after the intervention period than those in the control group. Moreover, based on the learning reports, evaluation tasks and interviews, it appears that the participants in the innovative program have become aware of their reading and how they improved their performance.
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The role of neuronal oscillations during language comprehension is not yet well understood. In this paper we review and reinterpret the functional roles of beta- and gamma-band oscillatory activity during language comprehension at the sentence and discourse level. We discuss the evidence in favor of a role for beta and gamma in unification (the unification hypothesis), and in light of mounting evidence that cannot be accounted for under this hypothesis, we explore an alternative proposal linking beta and gamma oscillations to maintenance and prediction (respectively) during language comprehension. Our maintenance/prediction hypothesis is able to account for most of the findings that are currently available relating beta and gamma oscillations to language comprehension, and is in good agreement with other proposals about the roles of beta and gamma in domain-general cognitive processing. In conclusion we discuss proposals for further testing and comparing the prediction and unification hypotheses.
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Oscillatory neural dynamics have been steadily receiving more attention as a robust and temporally precise signature of network activity related to language processing. We have recently proposed that oscillatory dynamics in the beta and gamma frequency ranges measured during sentence-level comprehension might be best explained from a predictive coding perspective. Under our proposal we related beta oscillations to both the maintenance/change of the neural network configuration responsible for the construction and representation of sentence-level meaning, and to top-down predictions about upcoming linguistic input based on that sentence-level meaning. Here we zoom in on these particular aspects of our proposal, and discuss both old and new supporting evidence. Finally, we present some preliminary magnetoencephalography data from an experiment comparing Dutch subject- and object-relative clauses that was specifically designed to test our predictive coding framework. Initial results support the first of the two suggested roles for beta oscillations in sentence-level language comprehension.
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"Speak the Future" presents a novel test case at the intersection of scientific innovation and public engagement. Leveraging the power of real-time AI image generation, the project empowers festival participants to verbally describe their visions for a sustainable and regenerative future. These descriptions are instantly transformed into captivating imagery using SDXL Turbo, fostering collective engagement and tangible visualisation of abstract sustainability concepts. This unique interplay of speech recognition, AI, and projection technology breaks new ground in public engagement methods. The project offers valuable insights into public perceptions and aspirations for sustainability, as well as understanding the effectiveness of AI-powered visualisation and regenerative applications of AI. Ultimately, this will serve as a springboard for PhD research that will aim to understand How AI can serve as a vehicle for crafting regenerative futures? By employing real-time AI image generation, the project directly tests its effectiveness in fostering public engagement with sustainable futures. Analysing participant interaction and feedback sheds light on how AI-powered visualisation tools can enhance comprehension and engagement. Furthermore, the project fosters public understanding and appreciation of research. The interactive and accessible nature of "Speak the Future" demystifies the research process, showcasing its relevance and impact on everyday life. Moreover, by directly involving the public in co-creating visual representations of their aspirations, the project builds an emotional connection and sense of ownership, potentially leading to continued engagement and action beyond the festival setting. "Speak the Future" promises to be a groundbreaking initiative, bridging the gap between scientific innovation and public engagement in sustainability discourse. By harnessing the power of AI for collective visualisation, the project not only gathers valuable data for researchers but also empowers the public to envision and work towards a brighter, more sustainable future.
